| --- |
| library_name: transformers |
| pipeline_tag: text-generation |
| inference: true |
| widget: |
| - text: Hello! |
| example_title: Hello world |
| group: Python |
| base_model: |
| - stepfun-ai/step3 |
| --- |
| |
| This tiny model is for debugging. It is randomly initialized with the config adapted from [stepfun-ai/step3](https://huggingface.co/stepfun-ai/step3). |
|
|
| Note: For vLLM supported version, see [tiny-random/step3-vllm](https://huggingface.co/tiny-random/step3-vllm). |
|
|
| ### Example usage: |
|
|
| ```python |
| import torch |
| from transformers import AutoModelForCausalLM, AutoProcessor |
| |
| model_id = "tiny-random/step3" |
| processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True) |
| model = AutoModelForCausalLM.from_pretrained( |
| model_id, |
| device_map="cuda", torch_dtype=torch.bfloat16, |
| trust_remote_code=True, |
| ) |
| messages = [ |
| { |
| "role": "user", |
| "content": [ |
| {"type": "image", "image": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg"}, |
| {"type": "text", "text": "What's in this picture?"} |
| ] |
| }, |
| ] |
| inputs = processor.apply_chat_template( |
| messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt" |
| ).to(model.device) |
| generate_ids = model.generate(**inputs, max_new_tokens=32, do_sample=False) |
| decoded = processor.decode(generate_ids[0, inputs["input_ids"].shape[-1]:], skip_special_tokens=False) |
| print(decoded) |
| ``` |
|
|
| ### Codes to create this repo: |
|
|
| ```python |
| import json |
| from pathlib import Path |
| |
| import accelerate |
| import torch |
| from huggingface_hub import file_exists, hf_hub_download |
| from transformers import ( |
| AutoConfig, |
| AutoModelForCausalLM, |
| AutoProcessor, |
| AutoTokenizer, |
| GenerationConfig, |
| set_seed, |
| ) |
| |
| source_model_id = "stepfun-ai/step3" |
| save_folder = "/tmp/tiny-random/step3" |
| |
| processor = AutoProcessor.from_pretrained(source_model_id, trust_remote_code=True) |
| processor.save_pretrained(save_folder) |
| |
| def rewrite_automap(filepath: str, source_model_id: str, overrides: dict = None): |
| import json |
| with open(filepath, 'r', encoding='utf-8') as f: |
| config = json.load(f) |
| for k, v in config['auto_map'].items(): |
| v = v.split('--')[-1] |
| config['auto_map'][k] = f'{source_model_id}--{v}' |
| if overrides is not None: |
| config.update(overrides) |
| with open(filepath, 'w', encoding='utf - 8') as f: |
| json.dump(config, f, indent=2) |
| |
| rewrite_automap(f'{save_folder}/processor_config.json', source_model_id) |
| rewrite_automap(f'{save_folder}/tokenizer_config.json', source_model_id) |
| |
| with open(hf_hub_download(source_model_id, filename='config.json', repo_type='model'), 'r', encoding='utf-8') as f: |
| config_json = json.load(f) |
| |
| for k, v in config_json['auto_map'].items(): |
| config_json['auto_map'][k] = f'{source_model_id}--{v}' |
| config_json['architectures'] = ["Step3VLForConditionalGeneration"] |
| config_json['text_config'].update({ |
| "hidden_size": 32, |
| "intermediate_size": 64, |
| "num_hidden_layers": 2, |
| "num_attention_heads": 2, |
| "num_attention_groups": 1, |
| "head_dim": 256, |
| "share_q_dim": 512, |
| "moe_layers_enum": "1", |
| "moe_num_experts": 8, |
| "moe_top_k": 3, |
| "moe_intermediate_size": 64, |
| "share_expert_dim": 64, |
| # "tie_word_embeddings": True, |
| }) |
| config_json['vision_config'].update({ |
| "hidden_size": 64, |
| "output_hidden_size": 64, |
| "intermediate_size": 128, |
| "num_hidden_layers": 2, |
| "num_attention_heads": 2 |
| }) |
| |
| with open(f"{save_folder}/config.json", "w", encoding='utf-8') as f: |
| json.dump(config_json, f, indent=2) |
| config = AutoConfig.from_pretrained( |
| save_folder, |
| trust_remote_code=True, |
| ) |
| print(config) |
| # key_mapping = { |
| # "^vision_model": "model.vision_model", |
| # r"^model(?!\.(language_model|vision_model))": "model.language_model", |
| # "vit_downsampler": "model.vit_downsampler", |
| # "vit_downsampler2": "model.vit_downsampler2", |
| # "vit_large_projector": "model.vit_large_projector", |
| # } |
| automap = config_json['auto_map'] |
| torch.set_default_dtype(torch.bfloat16) |
| model = AutoModelForCausalLM.from_config(config, trust_remote_code=True) |
| torch.set_default_dtype(torch.float32) |
| if file_exists(filename="generation_config.json", repo_id=source_model_id, repo_type='model'): |
| model.generation_config = GenerationConfig.from_pretrained( |
| source_model_id, trust_remote_code=True, |
| ) |
| set_seed(42) |
| model = model.cpu() # cpu is more stable for random initialization across machines |
| with torch.no_grad(): |
| for name, p in sorted(model.named_parameters()): |
| torch.nn.init.normal_(p, 0, 0.2) |
| print(name, p.shape) |
| model.save_pretrained(save_folder) |
| print(model) |
| rewrite_automap(f'{save_folder}/config.json', source_model_id) |
| |
| for python_file in Path(save_folder).glob('*.py'): |
| if python_file.name.startswith('modeling_') or python_file.name.startswith('configuration_') or python_file.name.endswith('.py'): |
| python_file.unlink() |
| ``` |
|
|
| ### Printing the model: |
|
|
| ```text |
| Step3vForConditionalGeneration( |
| (model): Step3vModel( |
| (vision_model): StepCLIPVisionTransformer( |
| (embeddings): StepCLIPVisionEmbeddings( |
| (patch_embedding): Conv2d(3, 64, kernel_size=(14, 14), stride=(14, 14)) |
| (position_embedding): Embedding(2705, 64) |
| ) |
| (transformer): StepCLIPEncoder( |
| (layers): ModuleList( |
| (0-1): 2 x StepCLIPEncoderLayer( |
| (layer_norm1): LayerNorm((64,), eps=1e-06, elementwise_affine=True) |
| (layer_norm2): LayerNorm((64,), eps=1e-06, elementwise_affine=True) |
| (self_attn): StepCLIPAttention( |
| (qkv_proj): Linear(in_features=64, out_features=192, bias=True) |
| (out_proj): Linear(in_features=64, out_features=64, bias=True) |
| ) |
| (mlp): StepCLIPMLP( |
| (fc1): Linear(in_features=64, out_features=128, bias=True) |
| (act): QuickGELUActivation() |
| (fc2): Linear(in_features=128, out_features=64, bias=True) |
| ) |
| ) |
| ) |
| ) |
| ) |
| (language_model): Step3Model( |
| (embed_tokens): Embedding(128815, 32) |
| (layers): ModuleList( |
| (0): Step3vDecoderLayer( |
| (self_attn): Step3vAttention( |
| (q_proj): Linear(in_features=32, out_features=512, bias=False) |
| (k_proj): Linear(in_features=32, out_features=256, bias=False) |
| (v_proj): Linear(in_features=32, out_features=256, bias=False) |
| (o_proj): Linear(in_features=512, out_features=32, bias=False) |
| (inter_norm): Step3vRMSNorm((512,), eps=1e-05) |
| (wq): Linear(in_features=512, out_features=512, bias=False) |
| ) |
| (mlp): Step3vMLP( |
| (gate_proj): Linear(in_features=32, out_features=64, bias=False) |
| (up_proj): Linear(in_features=32, out_features=64, bias=False) |
| (down_proj): Linear(in_features=64, out_features=32, bias=False) |
| (act_fn): SiLU() |
| ) |
| (input_layernorm): Step3vRMSNorm((32,), eps=1e-05) |
| (post_attention_layernorm): Step3vRMSNorm((32,), eps=1e-05) |
| ) |
| (1): Step3vDecoderLayer( |
| (self_attn): Step3vAttention( |
| (q_proj): Linear(in_features=32, out_features=512, bias=False) |
| (k_proj): Linear(in_features=32, out_features=256, bias=False) |
| (v_proj): Linear(in_features=32, out_features=256, bias=False) |
| (o_proj): Linear(in_features=512, out_features=32, bias=False) |
| (inter_norm): Step3vRMSNorm((512,), eps=1e-05) |
| (wq): Linear(in_features=512, out_features=512, bias=False) |
| ) |
| (moe): Step3vMoEMLP( |
| (gate): Linear(in_features=32, out_features=8, bias=False) |
| (up_proj): MoELinear() |
| (gate_proj): MoELinear() |
| (down_proj): MoELinear() |
| (act_fn): SiLU() |
| ) |
| (share_expert): Step3vMLP( |
| (gate_proj): Linear(in_features=32, out_features=64, bias=False) |
| (up_proj): Linear(in_features=32, out_features=64, bias=False) |
| (down_proj): Linear(in_features=64, out_features=32, bias=False) |
| (act_fn): SiLU() |
| ) |
| (input_layernorm): Step3vRMSNorm((32,), eps=1e-05) |
| (post_attention_layernorm): Step3vRMSNorm((32,), eps=1e-05) |
| ) |
| ) |
| (norm): Step3vRMSNorm((32,), eps=1e-05) |
| (rotary_emb): Step3vRotaryEmbedding() |
| ) |
| (vit_downsampler): Conv2d(64, 64, kernel_size=(2, 2), stride=(2, 2)) |
| (vit_downsampler2): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) |
| (vit_large_projector): Linear(in_features=128, out_features=32, bias=False) |
| ) |
| (lm_head): Linear(in_features=32, out_features=128815, bias=False) |
| ) |
| ``` |
